Integration of Hidden Markov Modeling and Bayesian Network for Fault Detection and Fault Prediction: An Automotive Case Study

Soleimani, Morteza; Campean, Felician; Neagu, Daniel; Doikin, Aleksandr

doi:10.3850/978-981-14-8593-0_5430-cd

Cited by 2 publications

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“…Where

N

is the number of states in an HMM,

D

is the number of different observations for each state,

A

is the state transition probability,

B

is the observation probability, and

π

is the initial state probability. HMM has been utilized in some applications adequately, for example, speech processing and handwriting recognition, and in the past two decades in industrial systems for diagnostics and prognostics 122‐125 . Practically, defining the discrete states for a continuous degrading system and estimating corresponding transition probabilities is one of the major challenges that restrict wider applications of HMM.…”

Section: Diagnostics and Prognostics Approachesmentioning

confidence: 99%

“…Multiple data‐driven models have also been broadly combined as another type of hybrid approach. In these approaches, one data‐driven method is used for feature extraction of a system, when it is not directly measurable (so‐called offline training), and the other data‐driven models are employed to predict the future state of the system based on the offline training 125,155‐158 The combination of data‐driven and physics‐based models are widely used among researchers for diagnostics and prognostics.…”

Section: Diagnostics and Prognostics Approachesmentioning

confidence: 99%

“…HMM has been utilized in some applications adequately, for example, speech processing and handwriting recognition, and in the past two decades in industrial systems for diagnostics and prognostics. [122][123][124][125] Practically, defining the discrete states for a continuous degrading system and estimating corresponding transition probabilities is one of the major challenges that restrict wider applications of HMM.…”

Section: Bayesian-based Modelsmentioning

confidence: 99%

“…In these approaches, one data-driven method is used for feature extraction of a system, when it is not directly measurable (so-called offline training), and the other data-driven models are employed to predict the future state of the system based on the offline training. 125,[155][156][157][158] • The combination of data-driven and physics-based models are widely used among researchers for diagnostics and prognostics. As these two models are individually capable for both diagnostics and prognostics, their different combinations have been exploited extensively in the literature.…”

Section: Hybrid Approachesmentioning

confidence: 99%

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Diagnostics and prognostics for complex systems: A review of methods and challenges

Soleimani

Campean

Neagu

2021

Quality & Reliability Eng

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Diagnostics and prognostics have a significant role in the reliability enhancement of systems and are focused topics of active research. Engineered systems are becoming more complex and are subjected to miscellaneous failure modes that impact adversely their performability. This ever‐increasing complexity makes fault diagnostics and prognostics challenging for the system‐level functions. A significant number of successes have been achieved and acknowledged in some review papers; however, these reviews rarely focused on application to complex engineered systems nor provided a systematic review of diverse techniques and approaches to address the related challenges. To bridge the gap, this paper first presents a review to systematically cover the general concepts and recent development of various diagnostics and prognostics approaches, along with their strengths and shortcomings for the application of diverse engineered systems. Afterwards, given the characteristics of complex systems, the applicability of different techniques and methods that are capable to address the features of complex systems are reviewed and discussed, and some of the recent achievements in the literature are introduced. Finally, the unaddressed challenges are discussed by taking into account the characteristics of automotive systems as an example of complex systems. In addition, future development and potential research trends are offered to address those challenges. Consequently, this review provides a systematic view of the state‐of‐the‐art and case studies with a reference value for scholars and practitioners.

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“…Where